Predicting one-year left ventricular mass index regression following transcatheter aortic valve replacement in patients with severe aortic stenosis: A new era is coming
Name
fcvm-10-1130152.pdf
Description
Published version
Size
42.47 MB
Format
Adobe PDF
Checksum (MD5)
4b4af7e4139d109cc47c28792ee62522
Author(s)
Asheghan, Mohammad Mostafa
Javadikasgari, Hoda
Attary, Taraneh
Rouhollahi, Amir
Straughan, Ross
Willi, James Noel
Awal, Rabina
Sabe, Ashraf
de la Cruz, Kim I
Nezami, Farhad R
Date Issued
April 4, 2023
Journal
Frontiers in Cardiovascular Medicine
Publisher
Frontiers Media SA
Citation
Asheghan MM, Javadikasgari H, Attary T, Rouhollahi A, Straughan R, Willi JN, Awal R, Sabe A, de la Cruz KI and Nezami FR (2023) Predicting one-year left ventricular mass index regression following transcatheter aortic valve replacement in patients with severe aortic stenosis: A new era is coming. Front. Cardiovasc. Med. 10:1130152.
Version
Final published version
Abstract
Aortic stenosis (AS) is the most common valvular heart disease in the western world, particularly worrisome with an ever-aging population wherein postoperative outcome for aortic valve replacement is strongly related to the timing of surgery in the natural course of disease. Yet, guidelines for therapy planning overlook insightful, quantified measures from medical imaging to educate clinical decisions. Herein, we leverage statistical shape analysis (SSA) techniques combined with customized machine learning methods to extract latent information from segmented left ventricle (LV) shapes. This enabled us to predict left ventricular mass index (LVMI) regression a year after transcatheter aortic valve replacement (TAVR). LVMI regression is an expected phenomena in patients undergone aortic valve replacement reported to be tightly correlated with survival one and five year after the intervention. In brief, LV geometries were extracted from medical images of a cohort of AS patients using deep learning tools, and then analyzed to create a set of statistical shape models (SSMs). Then, the supervised shape features were extracted to feed a support vector regression (SVR) model to predict the LVMI regression. The average accuracy of the predictions was validated against clinical measurements calculating root mean square error and R2 score which yielded the satisfactory values of 0.28 and 0.67, respectively, on test data. Our work reveals the promising capability of advanced mathematical and bioinformatics approaches such as SSA and machine learning to improve medical output prediction and treatment planning.
MIT Department
Harvard-MIT Program in Health Sciences and Technology
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3389/fcvm.2023.1130152
